Water quality degradation imposes economic costs in a variety of forms. The economic benefit of pollution controls is the reduction in these damage costs.
Control costs include expenditures by firms on pollution control practices and equipment, increased costs of goods for consumers, and government expendi- tures on monitoring and enforcement of pollution control policies. This chap- ter provides an introduction to methods and issues in obtaining valid and reliable estimates.1
Environmental policy impacts
Environmental policy impacts are generated through a chain of actions and reactions. Some of these involve interactions within the economy, some involve interactions within the environment, and some involve interactions between the environment and economy (Fig. 4.1). In standard analysis, the first reaction is that of producers to the implementation of the policy instru- ment. Typically, it is assumed that producers take actions to minimize the costs of compliance.2Possibilities for individual farmers include going out of business, relocating the farm business, changes in the mix of outputs pro- duced, and changes in production and pollution abatement practices. Given adjustment costs and learning by doing about new practices, the decisions made in the short run will generally differ from those made in the intermedi- ate to long run. Moreover (as discussed in Chapter 3), environmental policies may induce technical change that may bring about further changes over the long run in the technological structure and location of agricultural produc-
86 M. Ribaudo and J.S. Shortle
Government programme
Administration/monitoring and enforcement costs;
opportunity costs of government transfers
Changes in farm income Changes in farm
resource allocation
Changes in pollution flows
Changes in input demands/output supplies
Changes in
water quality Input/output price changes
Changes in economic surpluses of consumers and input suppliers
Changes in public health, recreational use, water treatment costs
Environmental benefits
Fig. 4.1. Benefit–cost linkages.
tion. A fundamental contribution of economics to environmental policy analysis is describing and forecasting such behavioural responses to policy initiatives. These forecasts play two crucial roles. One is to provide informa- tion for assessing the costs of changes in farm resource allocation. The second is to provide information on changes in agricultural production practices needed for forecasting changes in pollution loads from agricultural lands.
Additional impacts with economic welfare consequences may be gener- ated through economic linkages to input and output markets. If the number of hectares (or producers) directly affected is sufficiently large, then the prices of inputs (e.g. local farm labour or farmland) or farm products may change as a result of change in farmers’ demand for inputs. Increased (decreased) input prices would mean benefits (costs) to input suppliers, and would modify the costs in the target farm population. Increased (decreased) output prices would mean costs (benefits) for consumers, and would also modify the costs in the target farm population. Moreover, changes in input or output prices would lead to benefits or costs to farmers who are not directly affected by the envi- ronmental measures.
Environmental benefits are initiated by impacts on the volume and timing of pollution flows that result from changes in farm resource allocation. These in turn result in changes in chemical and biological attributes of water resources. Forecasting the impacts of changes in farm practices on environ- mental quality attributes requires the use of physical models linking farming practices to pollution loads, and to chemical and biological indicators of water quality. Suites of models have been developed for examining the rela- tionships between farm practices and pollution loads, and between pollution loads and chemical indicators of water quality (Novotny and Olem, 1994).
These models are the empirical forms of the notional ‘runoff ’ and ‘fate and transport’ presented in Chapter 5. Factors included in these models are illus- trated in Fig. 4.2.
Watershed or basin modelscan be used to predict how farming practices affect residual loads to receiving waters. While watershed models generally require a great deal of data, depending on the size of the watershed and the complexity of the water system, recent developments in computer hardware and software have allowed for large area simulations of hydrologic processes to be more easily accomplished. An example of a watershed model is the Soil and Water Assessment Tool (SWAT) (Arnold et al., 1995). SWAT is now used extensively in the US. Continued development of databases will allow models such as SWATto be used more easily in a policy setting.
Another model that is finding increased use in the US is the US Geological Survey’s SPARROW model (for Spatially-Referenced Regression on Watershed Attributes). SPARROWis a statistical model that relates stream-nutrient loads to upstream sources and land-surface characteristics (Preston and Brakebill, 1999). It has been used in the assessment of gulf hypoxia (Alexander et al., 2000) and in evaluating policy options for reducing nutrients to the Chesapeake Bay (Preston and Brakebill, 1999).
Benefits and Costs of Control Policies 87
Another model that is being used in the assessment of nutrient reduction strategies in the Chesapeake Bay is the Hydrologic Simulation Program –
FORTRAN(HSPF) (Donigian et al., 1994). The HSPF model allows the simula- tion of nutrient loading on the basis of information collected in the water- shed. Some other models that have been used in watershed studies include Agricultural Nonpoint Source model (AGNPS) (Young et al., 1987) and Simulator for Water Resources in Rural Basins model (SWRRB) (Williams et al., 1985).
Field-scale modelsprovide the ability to develop and evaluate farm man- agement strategies and policy instruments at a smaller and more detailed scale. Field-scale models generally represent a homogeneous land use, and are used to evaluate on-site performance of best management practices in terms of nutrient and pesticide leaching below the bottom of the crop’s root zone and surface runoff of chemicals and sediment past the edge of the farm field.
Some popular field-scale models include the Universal Soil Loss Equation (USLE) (Wischmeier and Smith, 1978), Chemicals, Runoff, and Erosion from Agricultural Management Systems model (CREAMS) (Knisel, 1980), Groundwater Loading Effects of Agricultural Management Systems model (GLEAMS) (Leonard et al., 1987), Erosion-Productivity Impact Calculator model (EPIC) (Williams et al., 1984), Pesticide Root Zone Model (PRZM) (Carsel et al., 1984) and Nitrogen Leaching and Economic Analysis Package model (NLEAP) (Shaffer et al., 1991). While such models cannot be used to estimate changes
88 M. Ribaudo and J.S. Shortle
MANAGEMENT INPUT
Precipitation – Rain – Snow Temperature
Radiation Wind
Relative humidity Pollutant-rainout – Nutrients – Pesticides – Particulates – Heavy Metals
Surface runoff
Sediment- adsorbed chemicals
Dissolved chemicals Detritus
INPUT TO ESTUARY
Subsurface flow
Dissolved chemicals WATERFLOW OUTPUT Land use
Pesticides
Cultural practices Nutrients
Watershed system properties Soils, topography, geology, vegetation, draining network
Volatilized pollutant Chemical spray
drift Dust-adsorbed
chemicals Evapotranspired
water
AIRSHED OUTPUT INPUT
TO ESTUARY NATURAL INPUT
Fig. 4.2. Factors influencing the behaviour and export of agricultural chemicals from an agricultural watershed. (Adapted from Bailey and Swank, 1983.)
in pollutant loadings to water resources, they can be used to compare alterna- tive policies in their ability to reduce pollutant loss from the field in a least-cost manner.
The USLEis widely used by the US Department of Agriculture (USDA) to estimate reductions in soil erosion from implementing conservation practices, and is used by USDA to enforce conservation compliance on highly erodible cropland. EPICestimates chemical loss from a field to surface water, ground- water and the atmosphere. It has been built into the USDA’s USMP agricul- tural sector model, enabling a direct link between a policy’s welfare impacts to producers and consumers and changes in the generation of pollutants (House et al., 1999).
Chemical indicators provided by watershed or field-scale models (e.g. total suspended solids, dissolved oxygen, total nitrogen, acidity, etc.) have long been standard measures of water quality but, increasingly, scientists are advocat- ing the use of biological indicators (e.g. the presence or relative abundance of indicator species; taxa richness) because trends in chemical indicators can be misleading. For example, standard chemical measures may be improving even while biological conditions are on the decline (Karr and Chu, 1999). Figure 4.3 is a conceptual model adapted from the US Environmental Protection Agency’s Waquoit Bay watershed risk assessment (US EPA, 1996). It illus- trates relationships between pollution loads, ecological impacts, and assess- ment impacts and measures. At the top of the model, human activities that cause environmental stresses in the watershed are shown in rectangles. These sources of stressors are linked to stressor types, depicted in ovals. Multiple types of stressor source are shown to contribute to an individual stressor. The stressors then lead to multiple ecological effects, depicted again in rectangles.
Some rectangles are double-lined to indicate effects that can be directly mea- sured for data analysis. Finally, the effects are linked to particular assessment endpoints. The connections show that one effect can result in changes in many assessment endpoints. A weak link in current assessment capacity is quantitative modelling of changes in biological endpoints in response to changes in stressors.
Table 4.1 illustrates types of benefits for freshwater quality changes, using a taxonomy developed by Mitchell and Carson (1989). To understand fully the impacts of water quality changes, and properly assess the types of benefits of water quality improvements illustrated in Table 4.1, additional economic modelling is needed. Specifically, changes in water quality will lead to changes in the use of water resources, and other economic responses that influence the benefits of water quality improvements (Smith, 1997). For example, improved freshwater quality in a lake that improves fishing condi- tions will enhance the experience of current anglers and may lead to an increase in their use of the resource. It may also lead to use of the resource by individuals who had been selecting other sites. These behavioural responses influence the benefits of the water quality improvement. Similarly, an improvement in the conditions of a commercial fishery can affect economic
Benefits and Costs of Control Policies 89
90M. Ribaudo and J.S. Shortle
Atmosphere Residential dev. Industry Marine activities
Agriculture Fertilizer application
Automotive
emissions Septic
systems
Flow control structures
Pesticide
application Industrial emissions
Toxic chemicals
Altered flow (riverine)
(Re)suspended sediments Wells
Nutrients Impervious surfaces Lawn and
garden maintenance
Construction (all)
Chemical point sources
Sewage treatment plant
Recreational boating
Docks, piers and marinas
Shoreline protection and modification Channel
maintenance
Shellfishing
Freshwater fishing
Offshore fishing
Disease
Physical alteration of habitat
Harvest pressure
ActivitiesSystem stressors
Benefits and Costs of Control Policies91
chemicals flow Nutrients Disease alteration
of habitat
Harvest pressure
Ecological effects
Change of flood and low flow
Decreased stream gravel
substrate SW + sed
toxics Shellfish/
fish tissue residue
Health assess-
ment index
Migratory fish (streams)
Egg, fly indices
Invertebrate
indices Piping plover, Least tern FW
benthic invertebrates
(pond, stream)
Water- dependent
wildlife
FW pond fish community
Pond IBI
Tropic state indices
Tropic status of
FW ponds Reduced
wetland area
Changed barrier beach area
Siltation Shading D.O.
Eelgrass decline
Loss of scallop habitat
Reduced saltmarsh
Loss of finfish habitat
Eelgrass habitat
Estuarine benthic invertebrate
community
Finfish community
Abundance of resident fish Clam,
scallop catch Increased
alga growth (macroalgae, phytoplankton,
epiphytes)
Surface water and sediment nutrients Physical
alteration of habitat(Re)suspended solids
Eelgrass cover
Fig. 4.3. Conceptual framework for watershed risk assessment. (Source: USEPA, 1996.)
welfare through several channels (Freeman, 1993). Initially, improved stocks may reduce the unit costs of fishing, increase the incomes of those who fish and increase harvests. Consumers would benefit as increased supplies lead to reduced prices. Changes in prices, costs and thus incomes from fishing would influence incentives for entry into or exit from the fishery. The resulting bene- fits to consumers and producers could be strongly affected by the economic structure of the industry (McConnell and Strand, 1989). Again, behavioural responses to the water quality improvement must be examined to assess the benefits fully. As above, it is generally not the impacts on particular individu- als or resulting from the decisions of particular individuals that are of inter- est. Rather, it is statistical (or probabilistic) outcomes on resource uses that are needed.
Valuation
The valuation problem can be developed in a fairly general way as follows.
Consider any individual affected by an agricultural environmental policy, because it affects either the individual’s income, the prices paid for market
92 M. Ribaudo and J.S. Shortle
Table 4.1. A typology of possible benefits from an improvement in freshwater quality.
Benefit Benefit
class category Benefit subcategory (examples)
Use Recreational (fishing, swimming, boating)
In-stream Commercial (fishing, navigation) Municipal (drinking water) Agriculture (irrigation)
Withdrawal Industrial/commercial (process treatment) Enhanced near-water recreation (hiking, picnicking)
Enhanced routine viewing (office/home views)
Aesthetic Enhanced recreation support (duck hunting) Ecosystem Enhanced general ecosystem support (food
chain)
Existence Vicarious Significant others (relatives, close friends) consumption Diffuse others (general public)
Inherent (preserving remote wetlands) Stewardship Bequest (family, future generations) Source: Mitchell and Carson (1989).
goods, or the flow of environmental services received. Examples would include: people receiving farm income; people purchasing farm products or goods produced using farm products; people purchasing goods for which envi- ronmental services are an input; people who receive income from the produc- tion of goods for which environmental services are an input; and people who directly consume environmental services. The utility function of the individ- ual is denoted u(x,q) where xis a vector of market consumption goods and q is a vector of environmental quality variables.3The vector xis selected by the consumer while qis exogenous. The utility function is continuous, increasing and quasi-concave in both vectors.
The consumer’s utility maximization problem is to choose xto maximize utility given qand the budget constraint px≤m, where pis a vector of prices for the consumption goods, and mis income. For the problem at hand, the income might be farm income, income from farm-related businesses, or income from a sector in which water quality is an input (e.g. commercial fish- eries). The consumer price vector would include the prices of food and other goods produced using farm output as inputs, and the prices of goods produced with environmental services as an input.
The indirect utility function corresponding to this problem is V(p, q, m)=ma
xx{u(x, q): px≤m}.
This function expresses the individual’s utility as a function of the prices paid for market consumption goods, income and environmental services. Changes in these variables are therefore the sources of economic benefits and costs and the policy relevant, at least for benefit/cost analysis, impacts.
Let p0, q0and m0denote the equilibrium values of p, qand mprior to an environmental policy intervention to reduce agriculture’s contribution to water pollution. The post intervention values are p1, q1and m1. For simplicity, we assume that q1≥q0. Thus the intervention does not reduce the level of any environmental quality variable and increases at least one.3 Accordingly, if none of the other exogenous determinants of the consumer’s welfare change, the consumer will be better off. We impose no structure on the changes in the other variables. It is possible that the environmental policy change may increase the price of some goods, the most obvious possibility being food, and reduce the price of others. Similarly, depending on the type of policy instru- ment (e.g. taxes, subsidies, regulations) and input and output prices, farm- related income may increase or decrease.
The Hicksian compensating measure of the economic benefit (cost) of the intervention, which we denote as b, is the amount of income that could be taken away from the consumer after the intervention such that utility after the intervention is the same as before. This amount is positive if the policy intervention increases the individual’s welfare (e.g. an individual who enjoys improved environmental quality and suffers no adverse impacts on prices or income) and thus a measure of benefit. It is the maximum amount the con- sumer would be willing to pay (WTP) for the intervention. Alternatively, if the
Benefits and Costs of Control Policies 93
policy intervention reduces the individual’s welfare (e.g. an individual who suffers from increased prices or reduced income), the Hicksian measure is negative and thus a measure of cost. The absolute value of the Hicksian measure in this case is the minimum amount the individual would be willing to accept (WTA) for the intervention.4Formally, we have
V(p0,m0,q0) =V(p1,m1b,q1)
where b=WTP ifbis positive and b=WTA ifbis negative.
Dual to the utility maximization problem is the problem of minimizing the expenditures required to attain a specified level of utility. The expenditure function corresponding to this problem is:
e(p,q,u) =mi
xn{px: u(x,q) ≥u}.
Using the expenditure function, bcan be expressed as:
b=e(p0,q0,u0) e(p1,q1,u0) +(m1m0) (1) where u0is the pre-intervention level of utility. Accordingly, an individual’s benefit (cost) is the reduction (increase) in income required to achieve the ini- tial utility level, plus the actual change in income. Given m0=e(p0,q0,u0), we can also write (1) as:
b=m0e(p1,q1,u0) +(m1m0). (2) We can also express b as the line integral (Boadway and Bruce, 1984;
Johansson, 1987):
(3)
where piis the ith element of p, hi(p,q,u) is the Hicksian demand for consumer good i, and qjis the jth element of q. The integrals under the first summation are the changes in the Hicksian compensating variationfor each good due to commodity price changes. These terms vanish if prices are unaffected. The integral in the second summation gives the Hicksian compensating surplusof the increase in environmental services. Thus, the benefit (cost) of the policy for the individual is given by the sum of changes in the Hicksian compensat- ing variation resulting from changes in the prices of market goods, the sum of the Hicksian compensating surplus due to changes in the levels of environ- mental services, and the change in income.5
b h p q u dp e
q q m m
i i
j j q
q
p j p
i j
j
i
= − i
( )
+ ∂∂ ∂
+
(
−)
∑ ∫
∑ ∫
, , 0 1 00 1
0 1
94 M. Ribaudo and J.S. Shortle
Methods for Estimating the Benefits of Water Quality Improvements
Benefits from water quality improvements may accrue because the productiv- ity of goods that use water quality or water quality-related environmental ser- vices is increased, or because individuals derive an increase in the welfare directly from the increase in environmental services.
The benefits of productivity improvement
When water quality is a factor in the production of a market good, there are two avenues through which benefits from water quality improvements can be obtained: (i) through changes in the price of the marketable good to con- sumers; and (ii) through changes in incomes received by resource owners.
Firstly, consider the case of a single firm in a competitive industry. The firm’s variable cost function is c(x,w,q) where xis output, wis an input price vector, and q is water quality. Let p be the output price. Water quality increases productivity, reducing production costs. Accordingly, we have
∂c/∂q≤0. Profit is given by:
π =pxc(x,w,q) FC
where FCis fixed costs. Given profit maximization, the firm will choose xsuch that p= ∂c/∂x. Let x*(p,w,q) denote the optimal choice of x. This quantity is the firm’s supply of the good given the market price p, factor price vector w and water quality level q. The firm’s profit function is:
π(p,w,q) =px*(p,w,q) c(x*(p,w, q), w,q) FC.
Changes in qwill affect the firm’s cost and profits, and thus the income resulting from production. An exact measure of the income change is the change in quasi-rents, where quasi-rents are revenues less variable costs (Just et al., 1982). Since fixed costs are fixed, the change in quasi-rents (∆) due to a change in water quality, and any related induced price change, is equal to the change in profits. Suppose the initial economic condition p = p0, w = w0, q = q0. After the water quality change, the new economic state is p = p0, w=w0, q=q1(for simplicity we have assumed product and factor prices to be unaffected). The change in quasi-rents is:
∆= π(p0,w0,q1) π(p0,w0,q0).
By the fundamental theorem of calculus, and using the envelope theorem, we can express ∆as:
∆ = − ∂
∫
qq01∂qcdq.Benefits and Costs of Control Policies 95